Image compression techniques involve exploiting aspects of an image to reduce its overall size while retaining information that can be used to re-establish the image to its original (lossless) or near-original (lossy) form. Different parameters can be provided to compressors to achieve performance characteristics that best-fit particular environments. For example, higher compression ratios can be used to increase the amount of available storage space within computing devices (e.g., smart phones, tablets, wearables, etc.), but this typically comes at a cost of cycle-intensive compression procedures that consume correspondingly higher amounts of power and time. On the contrary, cycle-efficient compression techniques can reduce power and time consumption, but this typically comes at a cost of correspondingly lower compression ratios and amounts of available storage space within computing devices.
Notably, new compression challenges are arising as computing device capabilities are improved through hardware and software advancements. For example, organic light-emitting diode (OLED) displays—which are becoming a popular choice for computing device displays—can degrade in a non-uniform manner over their lifespans and lead to unwanted color/brightness artifacts. To address this concern, burn-in statistics—which record historical usage information associated with a given OLED display—can be used to artificially adjust the operation of the OLED display to substantially restore visual uniformity throughout its operation. Notably, such burn-in statistics can take the form of a high-resolution, multiple-channel image that consumes a considerable amount of storage space within the computing device in which the OLED display is included. For obvious reasons, this consumption can dissatisfy users as their overall expected amount of available storage space is reduced for seemingly unknown reasons. It is therefore desirable to store the burn-in statistics in a more efficient manner.